Spatio-temporal change of support methods are designed for statistical analysis on spatial and temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-temporal models, which we refer to as STCOS, for the case of Gaussian outcomes. Application of STCOS methodology from this literature requires a level of proficiency with spatio-temporal methods and statistical computing which may be a hurdle for potential users. The present work seeks to bridge this gap by guiding readers through STCOS computations. We focus on the R computing environment because of its popularity, free availability, and high quality contributed packages. The stcos package is introduced to facilitate computations for the STCOS model. A motivating application is the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. The STCOS methodology offers a principled approach to compute model-based estimates and associated measures of uncertainty for ACS variables on customized geographies and/or time periods. We present a detailed case study with ACS data as a guide for change of support analysis in R, and as a foundation which can be customized to other applications.

支持方法的时空变化旨在对时空域进行统计分析，这可能与所观察到的数据有所不同。先前的工作介绍了一个简单的贝叶斯分层时空模型，对于高斯结果，我们将其称为STCOS。这些文献中STCOS方法论的应用要求一定水平的时空方法和统计计算能力，这可能是潜在用户的障碍。本工作试图通过引导读者进行STCOS计算来弥合这一差距。由于R运算环境的普及，免费提供以及高质量的贡献软件包，因此我们专注于R运算环境。该stcos引入软件包以方便STCOS模型的计算。一项具有启发性的应用是美国社区调查（ACS），这是一项由美国人口普查局管理的正在进行的调查，用于测量美国不同人口的关键社会经济和人口统计学变量。STCOS方法论提供了一种原则性方法，可针对定制地理位置和/或时间段上的ACS变量计算基于模型的估计值和相关的不确定性度量。我们使用ACS数据提供了一个详细的案例研究，以作为R中支持分析更改的指南，并可以为其他应用程序定制基础。